348 citations · 977 across the 19 of their papers we have counts for
19 papers
The Anchors Hierachy: Using the triangle inequality to survive high dimensional data
Andrew Moore
This paper is about metric data structures in high-dimensional or non-Euclidean space that permit cached sufficient statistics accelerations of learning algorithms. It has recently…
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed Continuous And Discrete Variables
Scott Davies, Andrew Moore
Recently developed techniques have made it possible to quickly learn accurate probability density functions from data in low-dimensional continuous space. In particular, mixtures o…
Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes
Andrew Moore, Jeff Schneider
This paper is about searching the combinatorial space of contingency tables during the inner loop of a nonlinear statistical optimization. Examples of this operation in various dat…
Interpolating Conditional Density Trees
Scott Davies, Andrew Moore
Joint distributions over many variables are frequently modeled by decomposing them into products of simpler, lower-dimensional conditional distributions, such as in sparsely connec…
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs
Purnamrita Sarkar, Andrew Moore
Recently there has been much interest in graph-based learning, with applications in collaborative filtering for recommender networks, link prediction for social networks and fraud…
Dual-Tree Fast Gauss Transforms
Dongryeol Lee, Alexander G. Gray, Andrew W. Moore
Kernel density estimation (KDE) is a popular statistical technique for estimating the underlying density distribution with minimal assumptions. Although they can be shown to achiev…